Removal Of Impulse Noise In Images Research Articles

Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty

The acquisition and transmission of magnetic resonance images are susceptible to noise, particularly impulse noise. Although the method based on the ℓ0-norm and overlapping group sparse total variation (ℓ0-OGSTV) is effective for impulse noise image restoration, it can only mitigate the staircase artifacts to a certain extent. To boost the impulse noise removal performance of ℓ0-OGSTV, we propose a new restoration model that consists of two terms. Specifically, in the first term, we keep using the ℓ0-norm as the data fidelity term to eliminate impulse noise. In the second term, we first introduce an overlapping group sparsity fractional-order total variation regularizer to eliminate staircase artifacts while preserving structural information. Then, we adopt the minimax-concave penalty to further accurately estimate the image edges. Finally, we employ an alternate direction method of multipliers to solve the proposed optimization model. Clinical experiments demonstrate its effectiveness in denoising medical images.

New Class of Conjugate Gradient Methods for Removing Impulse Noise Images

The conjugate coefficient optimal is the very establishment of a variety of conjugate gradient methods. This paper proposes a new class coefficient of conjugate gradient (CG) methods for impulse noise removal, which is based on the quadratic model. Our proposed method ensures descent independent of the accuracy of the line search and it is globally convergent under some conditions, Numerical experiments are also presented for the impulse noise removal in images.

A New Type Coefficient Conjugate on the Gradient Methods for Impulse Noise Removal in Images

The conjugate gradient methods are outstanding by choosing a suitable for coefficient conjugate. In this paper, a modified version of the conjugate gradient algorithm suggested by Hideaki and Yasushi [4] is proposed in order to show that the new method is globally convergent, under standard assumptions. To exemplify the efficiency of the new method, its performance is examined for impulse noise removal in images.

Image Impulse Noise Removal Using Cascaded Filtering Based on Overlapped Adaptive Gaussian Smoothing and Convolutional Refinement Networks

Impulse noise is often introduced to images when captured through image sensors due to sharp and sudden disturbances in the image signal, analog-to-digital converter errors, sensor temperature, etc., severely degrading their visual quality. Therefore, it is essential to develop an effective method to remove image noise. We propose a novel image denoising method for “salt-and-pepper” (SP) noise, using cascaded filtering based on overlapped adaptive Gaussian smoothing (OAGS) and the convolutional refinement networks (CRNs). First, the noisy input image can be preliminarily denoised by OAGS, where the noisy pixels are removed and recovered. The CRNs refine the result by restoring fine details for the denoised image. Through extensive experimental results, we demonstrate the proposed method substantially outperforms other state-of-the-art denoising methods, especially for high-density SP noise.

Learning deep CNNs for impulse noise removal in images

Deep learning has been widely applied in image processing and computer vision due to its powerful learning capability. Although some learning models have been proposed to suppress noise in images, most of them are developed for Gaussian noise and few are for impulse noise. This paper proposes an image recovery method based on deep convolutional neural networks for impulse noise removal. The proposed framework falls into two components: a classifier network which divides image pixels into noisy and noise-free, and a regression network which is trained for image reconstruction. In the regression network, the noise-free pixels identified by the classifier network together with the original noisy image are used for recovery of the noisy image. Furthermore, batch normalization is embedded to the network to improve denoising performance. Experimental results show that the proposed method can excellently remove impulse noise, providing clear performance improvements over other state-of-the-art denoising methods.

A Novel Image Impulse Noise Removal Algorithm Optimized for Hardware Accelerators

Images are often corrupted with noise during the image acquisition and transmission stage. Here, we propose a novel approach for the reduction of random-valued impulse noise in images and its hardware implementation on various state-of-the-art FPGAs. The presented algorithm consists of two stages in which the first stage detects whether pixels have been corrupted by impulse noise and the second stage performs a filtering operation on the detected noisy pixels. The human visual system is sensitive to the presence of edges in any image therefore the filtering stage consists of an edge preserving median filter which performs the filtering operation while preserving the underlying fine image features. Experimentally, it has been found that the proposed scheme yields a better Peak Signal-to-Noise Ratio (PSNR) compared to other existing median-based impulse noise filtering schemes. The algorithm is implemented using the high-level synthesis tool PARO as a highly parallel and deeply pipelined hardware design that simultaneously exploits loop level as well as instruction level parallelism with a very short latency of only few milliseconds for 16 bit images of size 512 × 512 pixels.

High quality impulse noise removal via non‐uniform sampling and autoregressive modelling based super‐resolution

The challenge of image impulse noise removal is to restore spatial details from damaged pixels using remaining ones in random locations. Most existing methods use all uncontaminated pixels within a local window to estimate the centred noisy one via a statistic way. These kinds of methods have two defects. First, all noisy pixels are treated as independent individuals and estimated by their neighbours one by one, with the correlation between their true values ignored. Second, the image structure as a natural feature is usually ignored. This study proposes a new denoising framework, in which all noisy pixels are jointly restored via non-uniform sampling and supervised piecewise autoregressive modelling based super-resolution. In this method, the noisy pixels are jointly estimated in groups through solving a well-designed optimisation problem, in which image structure feature is considered as an important constraint. Another contribution is that piecewise autoregressive model is not simply adopted but carefully designed so that all noise-free pixels can be used to supervise the model training and optimisation problem solving for higher accuracy. The experimental results demonstrate that the proposed method exhibits good denoising performance in a large noise density range (10–90%).

A Modified Iterative Alternating Direction Minimization Algorithm for Impulse Noise Removal in Images

Images are often corrupted by impulse noise. In this paper, an alternating direction minimization with continuation algorithm is modified and iteratively used to remove the impulse noise in images by exploring its self-similarity. A patch-based nonlocal operator and sparse representation are married in thel1-l1optimization model to be solved. Simulation results demonstrate that the proposed algorithm outperforms typical denoising methods in terms of preserving edges and textures for both salt-and-pepper noise and random-valued impulse noise. It can be also applied to suppress impulse noise-like artifacts in real mural images.

Similar neighbor criterion for impulse noise removal in images

In this paper, a new method is proposed for removing and restoring random-valued impulse noise in images. This approach is based on a similar neighbor criterion, in which any pixel to be considered as an original pixel it should have sufficient numbers of similar neighboring pixels in a set of filtering windows. Compared with other well known methods in the literature, this technique achieves superior performance in restoring heavily corrupted noisy images. Furthermore, it has low computational complexity, and equally effective in restoring corrupted color and gray-level images.

High performance detection filter for impulse noise removal in images

A high performance detection (HPD) filter is proposed for impulse noise removal in images. In this approach, the noisy pixels are detected iteratively through several phases, based on a set of unique similarity criteria. Simulation results show that the HPD filter outperforms others at medium to high noise rates and suppresses impulse noise effectively while preserving image details, even thin lines.